MLMA-Net：用于纺织品缺陷图像多标记目标检测的多级多注意学习
MLMA-Net: multi-level multi-attentional learning for multi-label object detection in textile defect images
摘要：
为了识别和分类纺织品缺陷，已经提出了基于深度学习的方法，并在单标签纺织品图像中取得了显着的成功。然而，由于多个缺陷和小尺寸缺陷的共存，检测纺织品图像中的多标签缺陷仍然具有挑战性。为了解决这些挑战，提出并构建了多级多注意力深度学习网络（MLMA-Net），以1）增加检测小尺寸缺陷的特征表示能力; 2）生成最大化注意缺陷状态的能力的区分表示，其利用针对多个缺陷的更高分辨率的特征图。最后，通过一个多标签的纺织品缺陷图像目标检测数据集（DHU-ML 1000）验证了该模型的有效性。结果表明，该网络提取了更多的独特特征，并具有更好的性能比最先进的方法在现实世界的工业数据集。
英文摘要：
For the sake of recognizing and classifying textile defects, deep learning-based methods have been proposed and achieved remarkable success in single-label textile images. However, detecting multi-label defects in a textile image remains challenging due to the coexistence of multiple defects and small-size defects. To address these challenges, a multi-level, multi-attentional deep learning network (MLMA-Net) is proposed and built to 1) increase the feature representation ability to detect small-size defects; 2) generate a discriminative representation that maximizes the capability of attending the defect status, which leverages higher-resolution feature maps for multiple defects. Moreover, a multi-label object detection dataset (DHU-ML1000) in textile defect images is built to verify the performance of the proposed model. The results demonstrate that the network extracts more distinctive features and has better performance than the state-of-the-art approaches on the real-world industrial dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
